Machine learning for particle identification & deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN
dc.contributor.advisor | Dietel, Thomas | |
dc.contributor.author | Viljoen, Christiaan Gerhardus | |
dc.date.accessioned | 2020-05-06T02:23:15Z | |
dc.date.available | 2020-05-06T02:23:15Z | |
dc.date.issued | 2019 | |
dc.date.updated | 2020-05-06T01:48:48Z | |
dc.description.abstract | This Masters thesis outlines the application of machine learning techniques, predominantly deep learning techniques, towards certain aspects of particle physics. Its two main aims: particle identification and high energy physics detector simulations are pertinent to research avenues pursued by physicists working with the ALICE (A Large Ion Collider Experiment) Transition Radiation Detector (TRD), within the Large Hadron Collider (LHC) at CERN (The European Organization for Nuclear Research). | |
dc.identifier.apacitation | Viljoen, C. G. (2019). <i>Machine learning for particle identification & deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN</i>. (). ,Faculty of Science ,Department of Physics. Retrieved from | en_ZA |
dc.identifier.chicagocitation | Viljoen, Christiaan Gerhardus. <i>"Machine learning for particle identification & deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN."</i> ., ,Faculty of Science ,Department of Physics, 2019. | en_ZA |
dc.identifier.citation | Viljoen, C.G. 2019. Machine learning for particle identification & deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN. . ,Faculty of Science ,Department of Physics. | en_ZA |
dc.identifier.ris | TY - Thesis / Dissertation AU - Viljoen, Christiaan Gerhardus AB - This Masters thesis outlines the application of machine learning techniques, predominantly deep learning techniques, towards certain aspects of particle physics. Its two main aims: particle identification and high energy physics detector simulations are pertinent to research avenues pursued by physicists working with the ALICE (A Large Ion Collider Experiment) Transition Radiation Detector (TRD), within the Large Hadron Collider (LHC) at CERN (The European Organization for Nuclear Research). DA - 2019 DB - OpenUCT DP - University of Cape Town KW - Physics LK - https://open.uct.ac.za PY - 2019 T1 - Machine learning for particle identification & deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN TI - Machine learning for particle identification & deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN UR - ER - | en_ZA |
dc.identifier.uri | https://hdl.handle.net/11427/31781 | |
dc.identifier.vancouvercitation | Viljoen CG. Machine learning for particle identification & deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN. []. ,Faculty of Science ,Department of Physics, 2019 [cited yyyy month dd]. Available from: | en_ZA |
dc.language.rfc3066 | eng | |
dc.publisher.department | Department of Physics | |
dc.publisher.faculty | Faculty of Science | |
dc.subject | Physics | |
dc.title | Machine learning for particle identification & deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN | |
dc.type | Master Thesis | |
dc.type.qualificationlevel | Masters | |
dc.type.qualificationname | MSc |